running-causalpy-experiments
running-causalpy-experiments is a Statistical modelling skill for AI agents, published by pymc-labs in the CausalPy repository on GitHub. Skillspace rates running-causalpy-experiments D (61/100) and it passed the safety scan; the rating is based on static analysis only.
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.
- Grade
- D
- Quality
- 61 / 100
- Safety
- Passed
- GitHub stars
- 1,189
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 88 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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